Machine Learning in the Enterprise

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  • Introduction
    • This module provides an overview of the course and its objectives.
  • Understanding the ML Enterprise Workflow
    • This module discusses the ML enterprise workflow and the purpose of each step.
  • Data in the Enterprise
    • This module reviews Google’s enterprise data management and governance tools: Feature Store, Data Catalog, Dataplex, and Analytics Hub.
  • Science of Machine Learning and Custom Training
    • This module reviews the art and science of machine learning and neural networks. We'll also discuss how to train custom ML models using Vertex AI.
  • Vertex Vizier Hyperparameter Tuning
    • In this module we discuss how to do hyperparameter tuning using Vertex AI Vizier.
  • Prediction and Model Monitoring Using Vertex AI
    • This module covers Vertex AI prediction and model monitoring. We'll first discuss batch and online predictions using pre-built and custom containers, then we'll review model monitoring, which is a service that helps manage the performance of your ML models.
  • Vertex AI Pipelines
    • This module discusses Vertex AI pipelines and how to build them to orchestrate your ML workflow.
  • Best Practices for ML Development
    • This module reviews best practices for a number of different machine learning processes in Vertex AI.
  • Course Summary
    • This module is a summary of the Machine Learning in the Enterprise course.
  • Series Summary
    • This module is a summary of the Machine Learning on Google Cloud course series.